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B. B. Hicks and J. D. Shannon

Abstract

Radioactive fallout data suggest that the concentration of pollutants in rainfall, while highly variable, might be described on the average by about an inverse half-power dependence on the amount of precipitation. Recent measurements of sulfur concentrations in summer rainfall collected at Argonne National Laboratory tend to support this contention, as do preliminary results derived from operations of the DOE precipitation chemistry network. The concept is extended to develop a bulk removal rate for airborne total sulfur by precipitation for use in regional dispersion modeling.

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B. B. Hicks and H. S. Goodman

Abstract

A system is described for the measurement of the atmospheric flux of water vapor near the surface, using a quartz-crystal oscillator as humidity sensor (as described by Gjessing et al.) and based on the Fluxatron technique. Results from a field test give an energy balance recovery ratio (LE + H)/(RG) = 1.00 ± 0.04.

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B. B. Hicks and G. D. Hess

Abstract

Gradients of temperature and humidity above water surfaces are analyzed in order to determine the dependence of the long-term average Bowen ratios β (the ratio of sensible to latent heat flux) on surface temperature. The least-squares fit that results from investigation of six such bodies of data, and which is supported by recent direct measurements of the fluxes by eddy correlation techniques, can be expressed as β = γ/s−0.15, where γ is the ratio of the specific heat of air at constant pressure to the latent heat of vaporization of water and s is the slope of the saturated specific humidity curve at the surface temperature. This expression forms the basis of a model which can be used to determine the average surface temperature from routine observations of air temperature and humidity at sea.

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B. B. Hicks and R. G. Everett

Abstract

No abstract available.

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B. B. Hicks and R. T. McMillen

Abstract

“Eddy accumulation” is a variation of standard eddy correlation techniques for determining eddy fluxes by sampling air in two separate systems depending on whether the vertical velocity is positive or negative. In concept, the corresponding eddy flux is determined directly from measurements of the pollutant concentration (or accumulation) difference between the two sampling systems. In practice, the method has not yet been demonstrated for a slowly-depositing pollutant.

A numerical simulation of the eddy accumulation technique has been used to test the sensitivity of the method to errors arising from various sources, including sensor orientation, sampling limitations and chemical resolution. These tests were conducted using artificial pollutant concentration signals derived from real meteorological data (obtained above a forest canopy), in order to avoid the possibility of injecting unwanted errors by employing a poor quality pollutant signal. To detect a pollutant deposition velocity of 0.1 cm s−1, it appears necessary to maintain linear sampling characteristics over a dynamic range corresponding to two orders of magnitude of vertical wind speed (the limits are approximately 0.05 σw and 5σw in any given condition, where σw is the standard deviation of the vertical velocity w), to maintain sampling zero offsets to less than 0.02σw of equivalent vertical velocity and to resolve chemical concentration differences amounting to about 0.4% in typical conditions.

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A. Hicks and B. M. Notaroš

Abstract

Taking advantage of the recent developments in machine learning, we propose an approach to automatic winter hydrometeor classification based on utilization of convolutional neural networks (CNNs). We describe the development, implementation, and evaluation of a method and tool for classification of snowflakes based on geometric characteristics and riming degree, respectively, obtained using CNNs from high-resolution images by a Multi-Angle Snowflake Camera (MASC). These networks are optimal for image classification of winter precipitation particles due to their high accuracy, computational efficiency, automatic feature extraction, and application versatility. They require little initial preparation, enable the use of smaller training sets through transfer learning techniques, come with large supporting communities and a wealth of resources available, and can be applied and operated by nonexperts. We illustrate both the ease of implementation and the usefulness of operation the CNN architecture offers as a tool for researchers and practitioners utilizing in situ optical observational devices. A training dataset containing 1450 MASC images is developed primarily from two storm events in December 2014 and February 2015 in Greeley, Colorado, by visual inspection of recognizable snowflake geometries. Defined geometric classes are aggregate, columnar crystal, planar crystal, small particle, and graupel. The CNN trained on this dataset achieves a mean accuracy of 93.4% and displays excellent generalization (ability to classify new data). In addition, a separate training dataset is developed by sorting snowflakes into three classes and showcasing distinct degrees of riming. The CNN riming degree estimator yields promising initial results but would benefit from larger training sets.

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Marvin L. Wesely and Bruce B. Hicks

Abstract

Temperature and humidity fluctuations at frequencies within the inertial subrange are found experimentally to be partially correlated in the surface boundary layer over warm wet surfaces. The spectral correlation coefficient, deduced from variances and covariances computed by analog electronics, is near unity in the flux-carrying eddies and decreases with increasing frequency, approximately as n½. As a result, optical refractive index fluctuations may have the false appearance of being strongly anisotropic in the inertial subrange.

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C. Key, A. Hicks, and B. M. Notaroš

Abstract

We present improvements over our previous approach to automatic winter hydrometeor classification by means of convolutional neural networks (CNNs), using more data and improved training techniques to achieve higher accuracy on a more complicated dataset than we had previously demonstrated. As an advancement of our previous proof of concept study, this work demonstrates broader usefulness of deep CNNs by using a substantially larger and more diverse dataset, which we make publicly available, from many more snow events. We describe the collection, processing, and sorting of this dataset of over 25 000 high-quality Multi-Angle Snowflake Camera (MASC) image chips split nearly evenly between five geometric classes: aggregate, columnar crystal, planar crystal, graupel, and small particle. Raw images were collected over 32 snowfall events between November 2014 and May 2016 near Greeley, Colorado, and were processed with an automated cropping and normalization algorithm to yield 224 × 224 pixel images containing possible hydrometeors. From the bulk set of over 8 400 000 extracted images, a smaller dataset of 14 793 images was sorted by image quality and recognizability (Q&R) using manual inspection. A presorting network trained on the Q&R dataset was applied to all 8 400 000+ images to automatically collect a subset of 283 351 good snowflake images. Roughly 5000 representative examples were then collected from this subset manually for each of the five geometric classes. With a higher emphasis on in-class variety than our previous work, the final dataset yields trained networks that better capture the imperfect cases and diverse forms that occur within the broad categories studied to achieve an accuracy of 96.2% on a vastly more challenging dataset.

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J. R. Garratt, B. B. Hicks, and R. A. Valigura

Abstract

No abstract available

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